{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:4F24B5IEAFSHWD2ZW73JOMRKDZ","short_pith_number":"pith:4F24B5IE","schema_version":"1.0","canonical_sha256":"e175c0f50401647b0f59b7f697322a1e716e8a55fa039d50e35af79616a549de","source":{"kind":"arxiv","id":"1805.08352","version":1},"attestation_state":"computed","paper":{"title":"Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Lena Reed, Marilyn Walker, Shereen Oraby, Shubhangi Tandon, Stephanie Lukin, T. S. Sharath","submitted_at":"2018-05-22T02:07:32Z","abstract_excerpt":"Natural language generators for task-oriented dialogue must effectively realize system dialogue actions and their associated semantics. In many applications, it is also desirable for generators to control the style of an utterance. To date, work on task-oriented neural generation has primarily focused on semantic fidelity rather than achieving stylistic goals, while work on style has been done in contexts where it is difficult to measure content preservation. Here we present three different sequence-to-sequence models and carefully test how well they disentangle content and style. We use a sta"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1805.08352","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-22T02:07:32Z","cross_cats_sorted":[],"title_canon_sha256":"000e697f9b635b545c15561b7865abd9f3ae1714dcbaefcc57a7e0993d2c370a","abstract_canon_sha256":"27c79c14f24a7553ffc765f2031671135f1a0cf74ebf33944a13fd6a1263193f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:26.821555Z","signature_b64":"vtg0dCCkBCEcff2aIps5oCjXH7CO1zA6fJrVDFaiOZKNRAJ25f5gvS9oyKl3pzHjuyPzlfudkMgHLQGLf/HrBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e175c0f50401647b0f59b7f697322a1e716e8a55fa039d50e35af79616a549de","last_reissued_at":"2026-05-18T00:15:26.820889Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:26.820889Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Lena Reed, Marilyn Walker, Shereen Oraby, Shubhangi Tandon, Stephanie Lukin, T. S. Sharath","submitted_at":"2018-05-22T02:07:32Z","abstract_excerpt":"Natural language generators for task-oriented dialogue must effectively realize system dialogue actions and their associated semantics. In many applications, it is also desirable for generators to control the style of an utterance. To date, work on task-oriented neural generation has primarily focused on semantic fidelity rather than achieving stylistic goals, while work on style has been done in contexts where it is difficult to measure content preservation. Here we present three different sequence-to-sequence models and carefully test how well they disentangle content and style. We use a sta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08352","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1805.08352","created_at":"2026-05-18T00:15:26.821001+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.08352v1","created_at":"2026-05-18T00:15:26.821001+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08352","created_at":"2026-05-18T00:15:26.821001+00:00"},{"alias_kind":"pith_short_12","alias_value":"4F24B5IEAFSH","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_16","alias_value":"4F24B5IEAFSHWD2Z","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_8","alias_value":"4F24B5IE","created_at":"2026-05-18T12:32:05.422762+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4F24B5IEAFSHWD2ZW73JOMRKDZ","json":"https://pith.science/pith/4F24B5IEAFSHWD2ZW73JOMRKDZ.json","graph_json":"https://pith.science/api/pith-number/4F24B5IEAFSHWD2ZW73JOMRKDZ/graph.json","events_json":"https://pith.science/api/pith-number/4F24B5IEAFSHWD2ZW73JOMRKDZ/events.json","paper":"https://pith.science/paper/4F24B5IE"},"agent_actions":{"view_html":"https://pith.science/pith/4F24B5IEAFSHWD2ZW73JOMRKDZ","download_json":"https://pith.science/pith/4F24B5IEAFSHWD2ZW73JOMRKDZ.json","view_paper":"https://pith.science/paper/4F24B5IE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.08352&json=true","fetch_graph":"https://pith.science/api/pith-number/4F24B5IEAFSHWD2ZW73JOMRKDZ/graph.json","fetch_events":"https://pith.science/api/pith-number/4F24B5IEAFSHWD2ZW73JOMRKDZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4F24B5IEAFSHWD2ZW73JOMRKDZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4F24B5IEAFSHWD2ZW73JOMRKDZ/action/storage_attestation","attest_author":"https://pith.science/pith/4F24B5IEAFSHWD2ZW73JOMRKDZ/action/author_attestation","sign_citation":"https://pith.science/pith/4F24B5IEAFSHWD2ZW73JOMRKDZ/action/citation_signature","submit_replication":"https://pith.science/pith/4F24B5IEAFSHWD2ZW73JOMRKDZ/action/replication_record"}},"created_at":"2026-05-18T00:15:26.821001+00:00","updated_at":"2026-05-18T00:15:26.821001+00:00"}